使用SDK在Azure函数中将Azure Blob存储到JSON

Question

I am trying to create a timer trigger azure function that takes data from blob, aggregates it, and puts the aggregates in a cosmosDB. I previously tried using the bindings in azure functions to use blob as input, which I was informed was incorrect (see this thread: Azure functions python no value for named parameter ).

I am now using the SDK and am running into the following problem:

import sys, os.path
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'myenv/Lib/site-packages')))
import json
import pandas as pd
from azure.storage.blob import BlockBlobService 

data = BlockBlobService(account_name='accountname', account_key='accountkey')
container_name = ('container')
generator = data.list_blobs(container_name)

for blob in generator:
print("{}".format(blob.name))
json = json.loads(data.get_blob_to_text('container', open(blob.name)))


df = pd.io.json.json_normalize(json)
print(df)

This results in an error:

IOError: [Errno 2] No such file or directory: 'test.json'

I realize this might be an absolute path issue, but im not sure how that works with azure storage. Any ideas on how to circumvent this?

---

Made it "work" by doing the following:

for blob in generator:
loader = data.get_blob_to_text('kvaedevdystreamanablob',blob.name,if_modified_since=delta)
json = json.loads(loader.content)

This works for ONE json file, ie I only had one in storage, but when more are added I get this error:

ValueError: Expecting object: line 1 column 21907 (char 21906)

This happens even if i add if_modified_since as to only take in one blob. Will update if I figure something out. Help always welcome.

---

Another update: My data is coming in through stream analytics, and then down to the blob. I have selected that the data should come in as arrays, this is why the error is occurring. When the stream is terminated, the blob doesnt immediately append ] to the EOF line in json, thus the json file isnt valid. Will try now with using line-by-line in stream analytics instead of array.

Answer 1

figured it out. In the end it was a quite simple fix:

I had to make sure each json entry in the blob was less than 1024 characters, or it would create a new line, thus making reading lines problematic.

The code that iterates through each blob file, reads and adds to a list is a follows:

data = BlockBlobService(account_name='accname', account_key='key')
generator = data.list_blobs('collection')

dataloaded = []
for blob in generator:
loader = data.get_blob_to_text('collection',blob.name)
trackerstatusobjects = loader.content.split('\n')
for trackerstatusobject in trackerstatusobjects:
    dataloaded.append(json.loads(trackerstatusobject))

From this you can add to a dataframe and do what ever you want :) Hope this helps if someone stumbles upon a similar problem.